GenBio AI’s cover photo
GenBio AI

GenBio AI

Research Services

Palo Alto, CA 29,685 followers

Building the World’s First AI-Driven Digital Organism (AIDO)

About us

GenBio.AI, Inc. (GenBio AI) is an innovative global startup dedicated to developing the world's first AI-driven Digital Organism, an integrated system of multiscale foundation models for predicting, simulating, and programming biology at all levels. Our goal is to achieve comprehensive, actionable empirical understandings of the mechanisms underlying all organismal physiologies and diseases. This will pave the way for a new paradigm in drug design, bio-engineering, personalized medicine, and fundamental biomedical research, all powered by Generative Biology. Our founding team consists of world-renowned scientists and researchers in AI and Biology from prestigious institutions such as CMU, MBZUAI, WIS, alongside prominent financial investors. GenBio AI, a true global effort from day one, is establishing offices in Palo Alto, Paris, and Abu Dhabi.

Website
https://genbio.ai/
Industry
Research Services
Company size
11-50 employees
Headquarters
Palo Alto, CA
Type
Privately Held
Founded
2024

Locations

Employees at GenBio AI

Updates

  • Don’t miss GenBio AI Co-Founder and Chief Scientific Advisor Emma Lundberg at #NVIDIAGTC: 🧬 Scaling Laws in Biology: Why Bigger Models Alone Aren’t Enough [S81652] Wednesday, March 18, 10:00–10:40 AM PT This in-person panel explores how progress in Bio x AI depends not just on larger models, but on scaling data generation, multimodal integration, and new training paradigms. Add it to your agenda: https://nvda.ws/3OAKT1T 🎟️ Register → https://nvda.ws/4cCOiY2

    View organization page for NVIDIA Healthcare

    68,748 followers

    🧬 From understanding disease to designing new medicines, Bio x AI is hitting a data wall and this #NVIDIAGTC session is all about how we break through it. Join leaders Emma Lundberg from GenBio AI, Ron Alfa, MD, PhD from NOETIK, Phil Lorenz from Basecamp Research, Bo Wang, and Daniel Burkhardt from NVIDIA as they explore how at-scale data generation and the three scaling laws (pre-training, post-training, and test-time scaling) are reshaping biological AI—from biodiversity-driven models to multi-modal human data pipelines. 💾 Save this session to your GTC agenda https://nvda.ws/3OAKT1T 🎟️ If you haven’t registered yet, now’s the time https://nvda.ws/4cCOiY2

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  • We love our new Paris office. Explore open roles across our teams in Palo Alto, Paris, and Abu Dhabi: https://genbio.ai/careers

    View organization page for Flashoffice

    5,716 followers

    🚀 Nouveaux bureaux trouvés pour GenBio AI ! Tout commence par une demande reçue directement via notre site internet 🌍 Adelaide nous contacte avec un brief clair 👉 trouver un bureau canon, capable d’accueillir jusqu’à 30 collaborateurs, pour accompagner la croissance de ses équipes. 🔬 GenBio AI, c’est une entreprise de biotechnologie et d’intelligence artificielle qui développe des modèles d’IA avancés pour simuler, prédire et analyser des systèmes biologiques complexes. Chez Flashoffice, c’est Louis qui prend le sujet en main 💪 Il part à la recherche de la perle rare dans notre portefeuille… et met rapidement la main sur l’ancien bureau de Finary, un match à 100 % avec le cahier des charges ✨ 📍 Visite avec Adelaide ➡️ coup de cœur immédiat. Et franchement, quand on voit les photos, on comprend pourquoi 👀 Les équipes de GenBio AI ont choisi Flashoffice pour : ✔️ gagner du temps ✔️ avoir une vision 360° du marché parisien ✔️ bénéficier d’un interlocuteur unique, du sourcing à la négociation 🎯 Résultat : Moins de 3 visites Un coup de cœur Un accompagnement de A à Z C’est exactement ça, la valeur ajoutée Flashoffice. Merci à GenBio AI de nous avoir fait confiance pour vous épauler dans cette recherche. Merci à Zacharie notre partenaire sur ce projet qui a géré les négociations en vacances ☀️ Si vous aussi vous recherchez un bureau à la hauteur de vos attentes, vous êtes au bon endroit 💙

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  • GenBio AI reposted this

    For foundation models in bio (or "virtual cells"), scale matters, but 𝘸𝘩𝘢𝘵 you scale may be even more important than 𝘩𝘰𝘸 𝘮𝘶𝘤𝘩 you scale. One interesting way to view GenBio AI's new preprint is as a systematic search over data space. While we often discuss the virtual cell in terms of model architectures or scaling laws, the real challenge may be identifying which data modalities hold the predictive signal for how a cell responds to interventions. By studying FMs trained on many different modalities, the study shows that models incorporating prior knowledge such as interactome networks, gene function annotations and curated causal scaffolds, clearly outperform those trained solely on sequence, structure or expression data alone. This performance gap is especially striking when comparing genetic knockout predictions (quite successful) versus small-molecule effect predictions (still challenging). This brings us to what I believe is the central challenge for building truly predictive and useful virtual cell models: 𝐖𝐡𝐚𝐭 𝐝𝐚𝐭𝐚 𝐭𝐨 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞? Single-cell sequencing data alone surely won't get us there. The best-performing models leverage decades of curated biological knowledge from functional experiments, network biology, and causal annotations. But how do we design iterative, efficient data generation schemes that can provide comparable value to this accumulated knowledge? How do we achieve adequate coverage across diverse biological contexts? I think we need completely new strategies for large-scale data generation in bio that systematically capture the causal, contextual relationships that underpin cellular behavior. #AI #Biology #VirtualCell #FoundationModels

    View profile for Elijah Cole

    Senior Research Scientist & Team Lead at GenBio AI

    Predicting how cells respond to genetic or chemical changes is a fundamental challenge in drug discovery. While the potential of biological Foundation Models (FMs) has been widely discussed, their actual superiority over simple statistical baselines has remained a subject of significant debate in the field. In our latest preprint at GenBio AI, we provide a definitive evaluation of FMs for perturbation prediction. By benchmarking over 600 model variants, we demonstrate that FMs, when trained on the right modalities and integrated effectively, provide a significant leap in predictive accuracy. Our findings confirm that FMs are not just a theoretical improvement, but a practical tool for building accurate, actionable simulations of cellular behavior. Preprint: https://lnkd.in/g7C2xpm7  Blog post: https://lnkd.in/gxF_bWXN Code and data: https://lnkd.in/gqHJb3We Joint work with Eric Xing, Ziv Bar-Joseph, Le Song, Emma Lundberg and many others!

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  • GenBio AI reposted this

    Predicting how cells respond to genetic or chemical changes is a fundamental challenge in drug discovery. While the potential of biological Foundation Models (FMs) has been widely discussed, their actual superiority over simple statistical baselines has remained a subject of significant debate in the field. In our latest preprint at GenBio AI, we provide a definitive evaluation of FMs for perturbation prediction. By benchmarking over 600 model variants, we demonstrate that FMs, when trained on the right modalities and integrated effectively, provide a significant leap in predictive accuracy. Our findings confirm that FMs are not just a theoretical improvement, but a practical tool for building accurate, actionable simulations of cellular behavior. Preprint: https://lnkd.in/g7C2xpm7  Blog post: https://lnkd.in/gxF_bWXN Code and data: https://lnkd.in/gqHJb3We Joint work with Eric Xing, Ziv Bar-Joseph, Le Song, Emma Lundberg and many others!

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  • Don’t miss GenBio AI Co-Founder and Chief Scientific Advisor Emma Lundberg at #NVIDIAGTC: 🧬 Scaling Laws in Biology: Why Bigger Models Alone Aren’t Enough [S81652] Wednesday, March 18, 10:00–10:40 AM PT This in-person panel explores how progress in Bio x AI depends not just on larger models, but on scaling data generation, multimodal integration, and new training paradigms. Add it to your agenda: https://nvda.ws/3OAKT1T 🎟️ Register → https://nvda.ws/4cCOiY2

    View organization page for NVIDIA Healthcare

    68,748 followers

    🧬 From understanding disease to designing new medicines, Bio x AI is hitting a data wall and this #NVIDIAGTC session is all about how we break through it. Join leaders Emma Lundberg from GenBio AI, Ron Alfa, MD, PhD from NOETIK, Phil Lorenz from Basecamp Research, Bo Wang, and Daniel Burkhardt from NVIDIA as they explore how at-scale data generation and the three scaling laws (pre-training, post-training, and test-time scaling) are reshaping biological AI—from biodiversity-driven models to multi-modal human data pipelines. 💾 Save this session to your GTC agenda https://nvda.ws/3OAKT1T 🎟️ If you haven’t registered yet, now’s the time https://nvda.ws/4cCOiY2

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  • In this video, GenBio AI Co-Founder and CTO Le Song shares how we are building a world model for biology to power simulation-driven discovery. Masdar City in Abu Dhabi serves as one of our core hubs, alongside our headquarters in Palo Alto and our hub in Paris. We are hiring across all three locations. Explore open roles → https://genbio.ai/careers

    View organization page for Masdar City Free Zone

    4,190 followers

    Scientific progress depends on the right ecosystem. For Le Song, CTO of GenBio AI and Professor at MBZUAI, proximity to research talent and collaboration were essential from day one. By operating from Masdar City Free Zone, GenBio AI gained direct access to leading AI expertise, strong partnerships, and the space to scale with stability. #MasdarCityFreeZone يعتمد التقدّم العلمي على وجود بيئة مناسبة تدعمه. بالنسبة إلى لي سونغ، الرئيس التنفيذي للتقنية في «جين بايو للذكاء الاصطناعي» وأستاذ في جامعة محمد بن زايد للذكاء الاصطناعي، كان القرب من الكفاءات البحثية وروح التعاون عاملاً مهماً منذ البداية. ومن خلال انطلاق الشركة من مدينة مصدر المنطقة الحرة، أصبحت أقرب إلى خبرات رائدة في الذكاء الاصطناعي وشراكات قوية، ما منحها أساساً ثابتاً للنمو والتوسّع. #مدينة_مصدر_المنطقة_الحرة

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